Yazar "Copty, Nadim K." seçeneğine göre listele

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    A COMBINED WAVELET AND NEURAL NETWORK MODEL FOR FORECASTING STREAMFLOW DATA
    (PARLAR SCIENTIFIC PUBLICATIONS (P S P), 2016) Yarar, Alpaslan; Onucyildiz, Mustafa; Copty, Nadim K.
    The modeling of streamflow is often needed for the sustainable management of water resources and for the protection against flooding. Over the years numerous streamflow forecasting models have been developed, black-box models, like Artificial Neural Networks (ANN), have became quite popular in the field of hydrologic engineering, because of their rapidity and less data requirements compared to physics-based models. In this study, a hybrid model, Wavelet-Neural Network (WNN), for the prediction of streamflow is developed. The model incorporates ANN and wavelet transform for the analysis of variations in streamflow time series. For demonstration, the model is applied to streamflow data from four flow observation stations (FOS), located in the West Mediterranean Basin of Turkey. Monthly mean streamflow data from the four FOS were used in the model. Original series were decomposed sub-series by wavelet transform. These sub-series were used for ANN model. In order to evaluate the performance of the WNN model, a multi regression (MR) model was also developed based on the same data set. Results show that WNN model forecasts the streamflow more accurately than the MR model with correlations between estimated and observed streamflow data ranging from 0.84-0.88.
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    Modelling level change in lakes using neuro-fuzzy and artificial neural networks
    (ELSEVIER SCIENCE BV, 2009) Yarar, Alpaslan; Onucyildiz, Mustafa; Copty, Nadim K.
    Accurate estimation of level change in lakes and reservoirs in response to climatic variations is an important step for the development of sustainable water usage policies, particularly for complex hydrological systems such as Lake Beysehir, Turkey. In this study, level changes of Lake Beysehir were estimated using adaptive neuro-fuzzy inference system (ANFIS), artificial neural networks (ANN) and a seasonal autoregressive integrated moving average (SARIMA). The ANN and ANFIS models were first trained based on observed data between 1966 and 1984, and then used to predict water level changes over the test period extending from 1985 to 1990. The performances of the different models were evaluated by comparing the corresponding values of mean squared errors (MSE) and decisive coefficients (R-2). While all models produced acceptable results, the minimum MSE value (0.0057) and the maximum R-2 value (0.7930) were obtained with ANFIS model, followed by the three-layered artificial neural network model (ANN1). The lowest performance was observed with the SARIMA model. (c) 2008 Elsevier B.V. All rights reserved.